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1. Bayesian Analysis in Health Economics

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Gianluca Baio
Andrea Berardi
Anna Heath
Nathan Green
Published in
Bayesian Cost-Effectiveness Analysis with the R package BCEA
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the application of Bayesian analysis in health economics, focusing on the evaluation of healthcare interventions. It begins by introducing the main aspects of Bayesian inference, including the underlying philosophy, practical issues in modeling, and computation. The chapter then explores the basics of health economic evaluation, emphasizing the use of Bayesian methods to model the complex relationships between clinical benefits and costs. A significant portion of the text is dedicated to Bayesian computation, particularly the use of Markov Chain Monte Carlo (MCMC) methods for simulating posterior and predictive distributions. The chapter also discusses the process of performing a Bayesian analysis and health economic evaluation in R, providing a step-by-step guide from pre-processing the data to post-processing the results. Additionally, it covers the use of the R package BCEA for decision and uncertainty analysis. The chapter concludes with a discussion on the advantages of using Bayesian methods in health economics, highlighting their flexibility and ability to incorporate prior knowledge and uncertainty into the analysis.
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next chapter Case Studies
Title
Bayesian Analysis in Health Economics
Authors
Gianluca Baio
Andrea Berardi
Anna Heath
Nathan Green
Copyright Year
2025
Book
Bayesian Cost-Effectiveness Analysis with the R package BCEA

Print ISBN: 978-3-032-00876-3

Electronic ISBN: 978-3-032-00877-0

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-032-00877-0_1
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